High-Performance Computing Center, Oklahoma State University, Stillwater, United States.
Department of Physiological Sciences, Oklahoma State University, Stillwater, United States.
Elife. 2023 Jan 19;12:e82980. doi: 10.7554/eLife.82980.
COVID19 has aptly revealed that airborne viruses such as SARS-CoV-2 with the ability to rapidly mutate combined with high rates of transmission and fatality can cause a deadly worldwide pandemic in a matter of weeks (Plato et al., 2021). Apart from vaccines and post-infection treatment options, strategies for preparedness will be vital in responding to the current and future pandemics. Therefore, there is wide interest in approaches that allow predictions of increase in infections ('surges') before they occur. We describe here real-time genomic surveillance particularly based on mutation analysis, of viral proteins as a methodology for a priori determination of surge in number of infection cases. The full results are available for SARS-CoV-2 at http://pandemics.okstate.edu/covid19/, and are updated daily as new virus sequences become available. This approach is generic and will also be applicable to other pathogens.
COVID19 恰当地揭示了，具有快速突变能力的空气传播病毒，如 SARS-CoV-2，结合高传播率和致死率，可能在数周内导致致命的全球大流行 (Plato 等人，2021)。除了疫苗和感染后治疗选择外，防范策略对于应对当前和未来的大流行至关重要。因此，人们广泛关注能够在感染增加（“激增”）之前进行预测的方法。我们在这里描述了实时基因组监测，特别是基于突变分析的病毒蛋白，作为事先确定感染病例数量激增的方法。SARS-CoV-2 的完整结果可在 http://pandemics.okstate.edu/covid19/ 获得，并随着新病毒序列的出现而每天更新。这种方法是通用的，也将适用于其他病原体。
